Memory estimation

Skill Red-Hat-AI-Innovation-Team/training_hub/.claude/skills/memory-estimation

Use when the user wants to estimate GPU memory (VRAM) requirements for a training configuration, check if a model will fit on their GPUs, or plan GPU allocation for training.From its SKILL.md

Install

npx -y skills add Red-Hat-AI-Innovation-Team/training_hub --skill memory-estimation

Assembled from the repository path, not quoted from the project. Check it against their README if it does not work.

SKILL.md

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GPU Memory Estimation

Estimate GPU VRAM requirements before committing to a training run.

Step 1: Check Environment

"${CLAUDE_PLUGIN_ROOT}/scripts/th_detect.sh"

If library=missing, tell the user to install training_hub first via the setup-guide skill.

Step 2: Run Estimation

Execute the estimation script with user-provided parameters or config defaults:

"${CLAUDE_PLUGIN_ROOT}/scripts/th_estimate.sh" $ARGUMENTS

Step 3: Present Results

Parse the JSON output and present clearly:

Memory estimates — Show low/mid/high VRAM estimates in GB
GPU fit — Report whether the configuration fits on the available GPU(s)
Recommendations — If memory is tight, suggest:
- Reduce max_seq_len (e.g., 4096 -> 2048)
- Reduce effective_batch_size
- Switch to LoRA or QLoRA for lower memory
- Add more GPUs for data parallelism

Estimation Methods

Method	For	Estimator
`basic`	SFT, GRPO	BasicEstimator
`osft`	OSFT	OSFTEstimator
`lora`	LoRA-SFT, LoRA-GRPO	LoRAEstimator
`qlora`	Quantized LoRA	QLoRAEstimator

If no method is specified, the script infers it from the configured algorithm.